<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Major sports events are watched by millions around the world and the prediction of event outcomes is a subject of interest to many stakeholders which underlines the relevance of continuous development and improvement of prediction models. This study uses a factorial design methodology to develop and test 18 Artificial Neural Network (ANN) models for the prediction of world championship boxing matches. The methodology was applied to evaluate the individual and collaborative effects of feature selection, ANN architecture and training data selection on the prediction performance of ANNs. Feature selection was found to be the most influential factor on prediction performance with a statistically significant Analysis of Variance (ANOVA) between the feature selection levels and the test accuracy (p-value of 0.012). The collaborative effect of training data selection and feature selection on prediction performance was found to be statistically significant with ANOVA p-value of 0.007. The best performing model achieved a test accuracy of 81.53% which is an improvement to current benchmarks for sports prediction. The findings of this study contribute to the development of future machine learning sports prediction models.</p>
</abstract>

Major sports events are watched by millions around the world and the prediction of event outcomes is a subject of interest to many stakeholders which underlines the relevance of continuous development and improvement of prediction models. This study uses a factorial design methodology to develop and test 18 Artificial Neural Network (ANN) models for the prediction of world championship boxing matches. The methodology was applied to evaluate the individual and collaborative effects of feature selection, ANN architecture and training data selection on the prediction performance of ANNs. Feature selection was found to be the most influential factor on prediction performance with a statistically significant Analysis of Variance (ANOVA) between the feature selection levels and the test accuracy (p-value of 0.012). The collaborative effect of training data selection and feature selection on prediction performance was found to be statistically significant with ANOVA p-value of 0.007. The best performing model achieved a test accuracy of 81.53% which is an improvement to current benchmarks for sports prediction. The findings of this study contribute to the development of future machine learning sports prediction models.

Developing a High-Performance Sports Results Prediction Artificial Neural Network: Case Study on World Championship Boxing

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Major sports events are watched by millions around the world and the prediction of event outcomes is a subject of interest to many stakeholders which...

Developing a High-Performance Sports Results Prediction Artificial Neural Network: Case Study on World Championship Boxing

Publicado en línea: 05 jun 2024

Páginas: 1 - 21

DOI: https://doi.org/10.2478/ijcss-2023-0008

Palabras clave
FACTORIAL DESIGN, FEATURE SELECTION, SPORTS RESULTS CLASSIFICATION, BOXING PREDICTION

© 2024 Sandisiwe Khanyisa Thisani, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Developing a High-Performance Sports Results Prediction Artificial Neural Network: Case Study on World Championship Boxing

Publicado en línea: 05 jun 2024

Páginas: 1 - 21

DOI: https://doi.org/10.2478/ijcss-2023-0008

Palabras claveFACTORIAL DESIGN, FEATURE SELECTION, SPORTS RESULTS CLASSIFICATION, BOXING PREDICTION

© 2024 Sandisiwe Khanyisa Thisani, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
FACTORIAL DESIGN, FEATURE SELECTION, SPORTS RESULTS CLASSIFICATION, BOXING PREDICTION